Advertisement
| question answering system natural language processing: Hands-on Question Answering Systems with BERT Navin Sabharwal, Amit Agrawal, 2021-02-06 Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning. The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you’ll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you’ll cover word embedding and their types along with the basics of BERT. After this solid foundation, you’ll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You’ll see different BERT variations followed by a hands-on example of a question answering system. Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT. What You Will Learn Examine the fundamentals of word embeddings Apply neural networks and BERT for various NLP tasks Develop a question-answering system from scratch Train question-answering systems for your own data Who This Book Is For AI and machine learning developers and natural language processing developers. |
| question answering system natural language processing: Open-Domain Question Answering John Prager, 2007 Open-Domain Question Answering is an introduction to the field of Question Answering (QA). It covers the basic principles of QA along with a selection of systems that have exhibited interesting and significant techniques, so it serves more as a tutorial than as an exhaustive survey of the field. Starting with a brief history of the field, it goes on to describe the architecture of a QA system before analysing in detail some of the specific approaches that have been successfully deployed by academia and industry designing and building such systems. Open-Domain Question Answering is both a guide for beginners who are embarking on research in this area, and a useful reference for established researchers and practitioners in this field. |
| question answering system natural language processing: The Oxford Handbook of Computational Linguistics Ruslan Mitkov, 2004 This handbook of computational linguistics, written for academics, graduate students and researchers, provides a state-of-the-art reference to one of the most active and productive fields in linguistics. |
| question answering system natural language processing: Dependency Parsing Sandra Kübler, Ryan McDonald, Joakim Nivre, 2022-05-31 Dependency-based methods for syntactic parsing have become increasingly popular in natural language processing in recent years. This book gives a thorough introduction to the methods that are most widely used today. After an introduction to dependency grammar and dependency parsing, followed by a formal characterization of the dependency parsing problem, the book surveys the three major classes of parsing models that are in current use: transition-based, graph-based, and grammar-based models. It continues with a chapter on evaluation and one on the comparison of different methods, and it closes with a few words on current trends and future prospects of dependency parsing. The book presupposes a knowledge of basic concepts in linguistics and computer science, as well as some knowledge of parsing methods for constituency-based representations. Table of Contents: Introduction / Dependency Parsing / Transition-Based Parsing / Graph-Based Parsing / Grammar-Based Parsing / Evaluation / Comparison / Final Thoughts |
| question answering system natural language processing: Computational Linguistics and Intelligent Text Processing Alexander Gelbukh, 2009-02-16 This book constitutes the refereed proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2009, held in Mexico City, Mexico in March 2009. The 44 revised full papers presented together with 4 invited papers were carefully reviewed and selected from numerous submissions. The papers cover all current issues in computational linguistics research and present intelligent text processing applications. |
| question answering system natural language processing: Experimental IR Meets Multilinguality, Multimodality, and Interaction Josanne Mothe, Jacques Savoy, Jaap Kamps, Karen Pinel-Sauvagnat, Gareth Jones, Eric San Juan, Linda Capellato, Nicola Ferro, 2015-08-31 This book constitutes the refereed proceedings of the 6th International Conference of the CLEF Initiative, CLEF 2015, held in Toulouse, France, in September 2015. The 31 full papers and 20 short papers presented were carefully reviewed and selected from 68 submissions. They cover a broad range of issues in the fields of multilingual and multimodal information access evaluation, also included are a set of labs and workshops designed to test different aspects of mono and cross-language information retrieval systems. |
| question answering system natural language processing: Evaluation of Multilingual and Multi-modal Information Retrieval Paul Clough, Fredric C. Gey, Jussi Karlgren, Bernardo Magnini, Douglas W. Oard, Maarten de Rijke, Maximilian Stempfhuber, 2007-09-04 This book constitutes the thoroughly refereed postproceedings of the 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006, held in Alicante, Spain, September 2006. The revised papers presented together with an introduction were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on Multilingual Textual Document Retrieval, Domain-Specifig Information Retrieval, i-CLEF, QA@CLEF, ImageCLEF, CLSR, WebCLEF and GeoCLEF. |
| question answering system natural language processing: Natural Language Processing and Chinese Computing Chengqing Zong, Jian-Yun Nie, Dongyan Zhao, Yansong Feng, 2014-12-09 This book constitutes the refereed proceedings of the Third CCF Conference, NLPCC 2014, held in Shenzhen, China, in December 2014. The 35 revised full papers presented together with 8 short papers were carefully reviewed and selected from 110 English submissions. The papers are organized in topical sections on fundamentals on language computing; applications on language computing; machine translation and multi-lingual information access; machine learning for NLP; NLP for social media; NLP for search technology and ads; question answering and user interaction; web mining and information extraction. |
| question answering system natural language processing: Advances in Open Domain Question Answering Tomek Strzalkowski, Sanda Harabagiu, 2006-10-07 Automated question answering - the ability of a machine to answer questions, simple or complex, posed in ordinary human language - is one of today’s most exciting technological developments. It has all the markings of a disruptive technology, one that is poised to displace the existing search methods and establish new standards for user-centered access to information. This book gives a comprehensive and detailed look at the current approaches to automated question answering. The level of presentation is suitable for newcomers to the field as well as for professionals wishing to study this area and/or to build practical QA systems. The book can serve as a how-to handbook for IT practitioners and system developers. It can also be used to teach advanced graduate courses in Computer Science, Information Science and related disciplines. The readers will acquire in-depth practical knowledge of this critical new technology. |
| question answering system natural language processing: Sentic Computing Erik Cambria, Amir Hussain, 2012-07-28 In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques is exploited on two common sense knowledge bases to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. |
| question answering system natural language processing: Natural Language Processing with Python Steven Bird, Ewan Klein, Edward Loper, 2009-06-12 This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify named entities Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful. |
| question answering system natural language processing: Speech and Language Processing Daniel Jurafsky, James H. Martin, 2000-01 This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing. |
| question answering system natural language processing: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| question answering system natural language processing: Hands-On Natural Language Processing with Python Rajesh Arumugam, Rajalingappaa Shanmugamani, 2018-07-18 Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow Key Features Weave neural networks into linguistic applications across various platforms Perform NLP tasks and train its models using NLTK and TensorFlow Boost your NLP models with strong deep learning architectures such as CNNs and RNNs Book Description Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts. What you will learn Implement semantic embedding of words to classify and find entities Convert words to vectors by training in order to perform arithmetic operations Train a deep learning model to detect classification of tweets and news Implement a question-answer model with search and RNN models Train models for various text classification datasets using CNN Implement WaveNet a deep generative model for producing a natural-sounding voice Convert voice-to-text and text-to-voice Train a model to convert speech-to-text using DeepSpeech Who this book is for Hands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book. |
| question answering system natural language processing: Advances in Information Communication Technology and Computing Vishal Goar, Manoj Kuri, Rajesh Kumar, Tomonobu Senjyu, 2020-08-18 This book features selected research papers presented at the International Conference on Advances in Information Communication Technology and Computing (AICTC 2019), held at the Government Engineering College Bikaner, Bikaner, India, on 8–9 November 2019. It covers ICT-based approaches in the areas ICT for energy efficiency, life cycle assessment of ICT, green IT, green information systems, environmental informatics, energy informatics, sustainable HCI and computational sustainability. |
| question answering system natural language processing: Natural Language Processing and Information Systems Farid Meziane, 2004-08-13 This book constitutes the refereed proceedings of the 9th International Conference on Applications of Natural Language to Information Systems, NLDB 2004, held in Salford, UK in June 2004. The 29 revised full papers and 13 revised short papers presented were carefully reviewed and selected from 65 submissions. The papers are organized in topical sections on natural language, conversational systems, intelligent querying, linguistic aspects of modeling, information retrieval, natural language text understanding, knowledge bases, knowledge management and content management. |
| question answering system natural language processing: Mathematics for Machine Learning Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, 2020-04-23 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. |
| question answering system natural language processing: Practical Natural Language Processing Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana, 2020-06-17 Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective |
| question answering system natural language processing: Natural Language Processing and Information Systems Max Silberztein, Faten Atigui, Elena Kornyshova, Elisabeth Métais, Farid Meziane, 2018-05-24 This book constitutes the refereed proceedings of the 23rd International Conference on Applications of Natural Language to Information Systems, NLDB 2018, held in Paris, France, in June 2018. The 18 full papers, 26 short papers, and 9 poster papers presented were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections: Opinion Mining and Sentiment Analysis in Social Media; Semantics-Based Models and Applications; Neural Networks Based Approaches; Ontology Engineering; NLP; Text Similarities and Plagiarism Detection; Text Classification; Information Mining; Recommendation Systems; Translation and Foreign Language Querying; Software Requirement and Checking. |
| question answering system natural language processing: Handbook of Linguistic Annotation Nancy Ide, James Pustejovsky, 2017-06-16 This handbook offers a thorough treatment of the science of linguistic annotation. Leaders in the field guide the reader through the process of modeling, creating an annotation language, building a corpus and evaluating it for correctness. Essential reading for both computer scientists and linguistic researchers.Linguistic annotation is an increasingly important activity in the field of computational linguistics because of its critical role in the development of language models for natural language processing applications. Part one of this book covers all phases of the linguistic annotation process, from annotation scheme design and choice of representation format through both the manual and automatic annotation process, evaluation, and iterative improvement of annotation accuracy. The second part of the book includes case studies of annotation projects across the spectrum of linguistic annotation types, including morpho-syntactic tagging, syntactic analyses, a range of semantic analyses (semantic roles, named entities, sentiment and opinion), time and event and spatial analyses, and discourse level analyses including discourse structure, co-reference, etc. Each case study addresses the various phases and processes discussed in the chapters of part one. |
| question answering system natural language processing: Natural Language Processing in Action Hannes Hapke, Cole Howard, Hobson Lane, 2019-03-16 Summary Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before. About the Book Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions. What's inside Some sentences in this book were written by NLP! Can you guess which ones? Working with Keras, TensorFlow, gensim, and scikit-learn Rule-based and data-based NLP Scalable pipelines About the Reader This book requires a basic understanding of deep learning and intermediate Python skills. About the Author Hobson Lane, Cole Howard, and Hannes Max Hapke are experienced NLP engineers who use these techniques in production. Table of Contents PART 1 - WORDY MACHINES Packets of thought (NLP overview) Build your vocabulary (word tokenization) Math with words (TF-IDF vectors) Finding meaning in word counts (semantic analysis) PART 2 - DEEPER LEARNING (NEURAL NETWORKS) Baby steps with neural networks (perceptrons and backpropagation) Reasoning with word vectors (Word2vec) Getting words in order with convolutional neural networks (CNNs) Loopy (recurrent) neural networks (RNNs) Improving retention with long short-term memory networks Sequence-to-sequence models and attention PART 3 - GETTING REAL (REAL-WORLD NLP CHALLENGES) Information extraction (named entity extraction and question answering) Getting chatty (dialog engines) Scaling up (optimization, parallelization, and batch processing) |
| question answering system natural language processing: Natural Language Processing and Information Retrieval Muskan Garg, Sandeep Kumar, Abdul Khader Jilani Saudagar, 2023-11-28 This book presents the basics and recent advancements in natural language processing and information retrieval in a single volume. It will serve as an ideal reference text for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology. This text emphasizes the existing problem domains and possible new directions in natural language processing and information retrieval. It discusses the importance of information retrieval with the integration of machine learning, deep learning, and word embedding. This approach supports the quick evaluation of real-time data. It covers important topics including rumor detection techniques, sentiment analysis using graph-based techniques, social media data analysis, and language-independent text mining. Features: • Covers aspects of information retrieval in different areas including healthcare, data analysis, and machine translation • Discusses recent advancements in language- and domain-independent information extraction from textual and/or multimodal data • Explains models including decision making, random walk, knowledge graphs, word embedding, n-grams, and frequent pattern mining • Provides integrated approaches of machine learning, deep learning, and word embedding for natural language processing • Covers latest datasets for natural language processing and information retrieval for social media like Twitter The text is primarily written for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology. |
| question answering system natural language processing: Web Information Retrieval Stefano Ceri, Alessandro Bozzon, Marco Brambilla, Emanuele Della Valle, Piero Fraternali, Silvia Quarteroni, 2013-08-30 With the proliferation of huge amounts of (heterogeneous) data on the Web, the importance of information retrieval (IR) has grown considerably over the last few years. Big players in the computer industry, such as Google, Microsoft and Yahoo!, are the primary contributors of technology for fast access to Web-based information; and searching capabilities are now integrated into most information systems, ranging from business management software and customer relationship systems to social networks and mobile phone applications. Ceri and his co-authors aim at taking their readers from the foundations of modern information retrieval to the most advanced challenges of Web IR. To this end, their book is divided into three parts. The first part addresses the principles of IR and provides a systematic and compact description of basic information retrieval techniques (including binary, vector space and probabilistic models as well as natural language search processing) before focusing on its application to the Web. Part two addresses the foundational aspects of Web IR by discussing the general architecture of search engines (with a focus on the crawling and indexing processes), describing link analysis methods (specifically Page Rank and HITS), addressing recommendation and diversification, and finally presenting advertising in search (the main source of revenues for search engines). The third and final part describes advanced aspects of Web search, each chapter providing a self-contained, up-to-date survey on current Web research directions. Topics in this part include meta-search and multi-domain search, semantic search, search in the context of multimedia data, and crowd search. The book is ideally suited to courses on information retrieval, as it covers all Web-independent foundational aspects. Its presentation is self-contained and does not require prior background knowledge. It can also be used in the context of classic courses on data management, allowing the instructor to cover both structured and unstructured data in various formats. Its classroom use is facilitated by a set of slides, which can be downloaded from www.search-computing.org. |
| question answering system natural language processing: Reasoning Web. Reasoning and the Web in the Big Data Era Manolis Koubarakis, Giorgos Stamou, Giorgos Stoilos, Ian Horrocks, Phokion Kolaitis, Georg Lausen, Gerhard Weikum, 2014-09-03 This volume contains the lecture notes of the 10th Reasoning Web Summer School 2014, held in Athens, Greece, in September 2014. In 2014, the lecture program of the Reasoning Web introduces students to recent advances in big data aspects of semantic web and linked data, and the fundamentals of reasoning techniques that can be used to tackle big data applications. |
| question answering system natural language processing: Deep Learning for Natural Language Processing Jason Brownlee, 2017-11-21 Deep learning methods are achieving state-of-the-art results on challenging machine learning problems such as describing photos and translating text from one language to another. In this new laser-focused Ebook, finally cut through the math, research papers and patchwork descriptions about natural language processing. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects. |
| question answering system natural language processing: Computational Science and Technology Rayner Alfred, Hiroyuki Iida, Haviluddin Haviluddin, Patricia Anthony, 2021-03-15 This book gathers the proceedings of the Seventh International Conference on Computational Science and Technology 2020 (ICCST 2020), held in Pattaya, Thailand, on 29–30 August 2020. The respective contributions offer practitioners and researchers a range of new computational techniques and solutions, identify emerging issues, and outline future research directions, while also showing them how to apply the latest large-scale, high-performance computational methods. |
| question answering system natural language processing: Advances in Artificial Intelligence - IBERAMIA 2008 Hector Geffner, Rui Prada, Isabel Machado Alexandre, Nuno David, 2008-10-01 IBERAMIA is the international conference series of the Ibero-American Art- cialIntelligencecommunitythathasbeenmeetingeverytwoyearssincethe1988 meeting in Barcelona. The conference is supported by the main Ibero-American societies of AI and provides researchers from Portugal, Spain, and Latin Am- ica the opportunity to meet with AI researchers from all over the world. Since 1998, IBERAMIA has been a widely recognized international conference, with its papers written and presented in English, and its proceedings published by Springer in the LNAI series. This volume contains the papers accepted for presentation at Iberamia 2008, held in Lisbon, Portugal in October 2008. For this conference, 147 papers were submitted for the main track, and 46 papers were accepted. Each submitted paper was reviewed by three members of the Program Committee (PC), coor- nated by an Area Chair. In certain cases, extra reviewerswererecruited to write additional reviews. The list of Area Chairs, PC members, and reviewers can be found on the pages that follow. The authors of the submitted papers represent 14 countries with topics c- ering the whole spectrum of themes in AI: robotics and multiagent systems, knowledge representation and constraints, machine learning and planning, n- ural language processing and AI applications. TheprogramforIberamia2008alsoincludedthreeinvitedspeakers:Christian Lemaitre (LANIA, M ́ exico), R. Michael Young (NCSU, USA) and Miguel Dias (Microsoft LDMC, Lisbon) as well as ?ve workshops. |
| question answering system natural language processing: Soft Computing for Problem Solving Jagdish Chand Bansal, 2019 This two-volume book presents outcomes of the 7th International Conference on Soft Computing for Problem Solving, SocProS 2017. This conference is a joint technical collaboration between the Soft Computing Research Society, Liverpool Hope University (UK), the Indian Institute of Technology Roorkee, the South Asian University New Delhi and the National Institute of Technology Silchar, and brings together researchers, engineers and practitioners to discuss thought-provoking developments and challenges in order to select potential future directions The book presents the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers in the areas including, but not limited to, algorithms (artificial immune systems, artificial neural networks, genetic algorithms, genetic programming, and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It is a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task. |
| question answering system natural language processing: The Process of Question Answering Wendy G. Lehnert, 2022-11-01 Originally published in 1978, The Process of Question Answering examines a phenomenon that relies on many realms of human cognition: language comprehension, memory retrieval, and language generation. Problems in computational question answering assume a new perspective when question answering is viewed as a problem in natural language processing. A theory of human question answering must necessarily entail a theory of human memory organization and theories of the cognitive processes that access and manipulate information in memory. This book describes question answering as a particular task in information processing. The theoretical models described here have been built on a formulation of general theories in natural language processing: theories about language that were developed without the specific problem of question answering in mind. By requiring programmers to be concerned with the precise form of information in memory, and the precise operations manipulating that information, they can uncover significant problems that would otherwise be overlooked. An early insight into artificial intelligence, today this reissue can be enjoyed in its historical context. |
| question answering system natural language processing: Mastering Natural Language Processing with Python Deepti Chopra, Nisheeth Joshi, Iti Mathur, 2016-06-10 Maximize your NLP capabilities while creating amazing NLP projects in PythonAbout This Book* Learn to implement various NLP tasks in Python* Gain insights into the current and budding research topics of NLP* This is a comprehensive step-by-step guide to help students and researchers create their own projects based on real-life applicationsWho This Book Is ForThis book is for intermediate level developers in NLP with a reasonable knowledge level and understanding of Python.What You Will Learn* Implement string matching algorithms and normalization techniques* Implement statistical language modeling techniques* Get an insight into developing a stemmer, lemmatizer, morphological analyzer, and morphological generator* Develop a search engine and implement POS tagging concepts and statistical modeling concepts involving the n gram approach* Familiarize yourself with concepts such as the Treebank construct, CFG construction, the CYK Chart Parsing algorithm, and the Earley Chart Parsing algorithm* Develop an NER-based system and understand and apply the concepts of sentiment analysis* Understand and implement the concepts of Information Retrieval and text summarization* Develop a Discourse Analysis System and Anaphora Resolution based systemIn DetailNatural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning.This book will give you expertise on how to employ various NLP tasks in Python, giving you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and assist you in creating your own NLP projects using NLTK.You will sequentially be guided through applying machine learning tools to develop various models. We'll give you clarity on how to create training data and how to implement major NLP applications such as Named Entity Recognition, Question Answering System, Discourse Analysis, Transliteration, Word Sense disambiguation, Information Retrieval, Sentiment Analysis, Text Summarization, and Anaphora Resolution. |
| question answering system natural language processing: Applied Natural Language Processing in the Enterprise Ankur A Patel, Ajay Uppili Arasanipalai, 2021-04-13 NLP is one of the hottest topics in AI today. Having lagged for years behind other deep learning fields such as computer vision, NLP only recently gained mainstream popularity. Google, Facebook, and OpenAI have open-sourced large pretrained language models, but many organizations today still struggle with building and adopting NLP applications. This hands-on guide helps you learn the process quickly. If you have a basic to intermediate understanding of machine learning and programming experience with Python, you'll learn how to build and deploy real-world NLP applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai walk you through the process without bogging you down in theory. Understand how state-of-the-art NLP models work Learn the tools of the trade, including frameworks popular today Perform NLP tasks such as text classification, semantic search, and reading comprehension Solve problems using new models like transformers and techniques such as transfer learning Build NLP models from scratch with performance comparable or superior to out-of-the-box systems Deploy your models to production and maintain their performance Implement a suite of NLP algorithms using Python and PyTorch |
| question answering system natural language processing: Multilingual Natural Language Processing Applications Daniel M. Bikel, Imed Zitouni, 2012 Global organizations must quickly and cost-effectively analyze, translate, synthesize, and distill massive amount of text in multiple languages. The technology needed to automate this process - multilingual natural language processing (NLP)- is advancing rapidly. This is the first comprehensive, one-stop-shop guide to building robust and accurate multilingual NLP systems. Multilingual Natural Language Applicationscombines all the essential background and realistic, up-to-date guidance practitioners will need to succeed. Containing new contributions from leading researchers at IBM, Google, Stanford, CMU, Columbia, and ISI, it integrates cutting-edge advances with practical solutions drawn from extensive field experience. Part I focuses primarily on multilingual NLP's core technologies, including technologies for understanding the structure of words and documents; analyzing syntax; modeling language; recognizing entailment, and detecting redundancy. Part II delves into the theoretical and practical considerations involved in using these technologies to construct real-world applications. It contains detailed chapters on information extraction, machine translation, information retrieval and search, summarization, question answering, distillation, and processing pipelines. |
| question answering system natural language processing: Development of IR Evaluation Methods Stephen Edward Robertson, 1999 |
| question answering system natural language processing: Advanced Machine Learning Technologies and Applications Aboul Ella Hassanien, Roheet Bhatnagar, Ashraf Darwish, 2020-05-25 This book presents the refereed proceedings of the 5th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2020), held at Manipal University Jaipur, India, on February 13 – 15, 2020, and organized in collaboration with the Scientific Research Group in Egypt (SRGE). The papers cover current research in machine learning, big data, Internet of Things, biomedical engineering, fuzzy logic and security, as well as intelligence swarms and optimization. |
| question answering system natural language processing: New Directions in Question Answering Mark T. Maybury, 2004 Major trends in the development of an important new method of information access that combines elements of natural language processing, information retrieval, and human computer interaction. Question answering systems, which provide natural language responses to natural language queries, are the subject of rapidly advancing research encompassing both academic study and commercial applications, the most well-known of which is the search engine Ask Jeeves. Question answering draws on different fields and technologies, including natural language processing, information retrieval, explanation generation, and human computer interaction. Question answering creates an important new method of information access and can be seen as the natural step beyond such standard Web search methods as keyword query and document retrieval. This collection charts significant new directions in the field, including temporal, spatial, definitional, biographical, multimedia, and multilingual question answering. After an introduction that defines essential terminology and provides a roadmap to future trends, the book covers key areas of research and development. These include current methods, architecture requirements, and the history of question answering on the Web; the development of systems to address new types of questions; interactivity, which is often required for clarification of questions or answers; reuse of answers; advanced methods; and knowledge representation and reasoning used to support question answering. Each section contains an introduction that summarizes the chapters included and places them in context, relating them to the other chapters in the book as well as to the existing literature in the field and assessing the problems and challenges that remain. |
| question answering system natural language processing: Practical Natural Language Processing Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana, 2020-06-17 Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective |
| question answering system natural language processing: NLP Natural Language Processing- A Complete Overciew Code Xtracts, 2023-06-11 NLP Natural Language Processing- A Complete Overciew for Engineering, BCA abd BSC Computer Courses; BCA Semester, Engineering Semester, BSC Computer Semester |
| question answering system natural language processing: NATURAL LANGUAGE PROCESSING BHARATI AKSHAR, VINEET CHAITANYA, SANGAL, RAJEEV, 1995-01-01 This book presents a Paninian perspective towards natural language processing. It has three objectives: (1) to introduce the reader to NLP, (2) to introduce the reader to Paninian Grammar (PG) which is the application of the original Paninian framework to the processing of modern Indian languages using the computer, (3) to compare Paninian Grammar (PG) framework with modern Western computational grammar frameworks.Indian languages like many other languages of the world have relatively free word order. They also have a rich system of case-endings and post-positions. In contrast to this, the majority of grammar frameworks and designed for English and other positional languages. The unique aspect of the computational grammar describes here is that it is designed for free word order languages and makes special use of case-endings and post-positions. Efficient parsers for the grammar are also described. The computational grammar is likely to be suitable for other free word order languages of the world.Second half of the book presents a comparison of Paninian Grammar (PG) with existing modern western computational grammars. It introduces three western grammar frameworks using examples from English: Lexical Functional Grammar (LFG), Tree Adjoining Grammar (TAG), and Government and Binding (GB). The presentation does not assume any background on part of the reader regarding these frameworks. Each presentation is followed by either a discussion on applicability of the framework to free word order languages, or a comparison with PG framework. |
| question answering system natural language processing: Proceedings of the Third International Conference on Computational Intelligence and Informatics K. Srujan Raju, A. Govardhan, B. Padmaja Rani, R. Sridevi, M. Ramakrishna Murty, 2020-03-17 This book features high-quality papers presented at the International Conference on Computational Intelligence and Informatics (ICCII 2018), which was held on 28–29 December 2018 at the Department of Computer Science and Engineering, JNTUH College of Engineering, Hyderabad, India. The papers focus on topics such as data mining, wireless sensor networks, parallel computing, image processing, network security, MANETS, natural language processing and Internet of things. |
Which of 'Question on', 'question about', 'question regarding ...
"a question on" means: "a question on the topic of" and therefore can only be used when one can insert the phrase "the topic of" after the "on", while "a question about" can used before …
Conversation Questions for the ESL/EFL Classroom (I-TESL-J)
Interesting questions for discussions in Engish lessons. A Project of The Internet TESL Journal If this is your first time here, then read the Teacher's Guide to Using These Pages
When to use "is" vs. "does" when asking a question?
When the verb in a statement is neither a primary auxiliary verb (be, have, do) nor a modal auxiliary verb (will, would, can, could, may, might, shall, should, must, ought to, used to), do is …
ESL Conversation Questions - Restaurants & Eating Out (I-TESL-J)
Restaurants & Eating Out A Part of Conversation Questions for the ESL Classroom.. Related: Fruits and Vegetables, Vegetarian, Diets, Food & Eating, Tipping
ESL Conversation Questions - Food & Eating (I-TESL-J)
Food & Eating A Part of Conversation Questions for the ESL Classroom.. Related: Restaurants, Fruits and Vegetables, Vegetarian, Diets, Tipping
ESL Conversation Questions - The Art of Conversation (I-TESL-J)
A list of questions you can use to generate conversations in the ESL/EFL classroom.
ESL Conversation Questions - Hobbies (I-TESL-J)
A list of questions you can use to generate conversations in the ESL/EFL classroom.
ESL Conversation Questions - Do You Wish...? (I-TESL-J)
A list of questions you can use to generate conversations in the ESL/EFL classroom.
ESL Conversation Questions - English study (I-TESL-J)
English Study A Part of Conversation Questions for the ESL Classroom.. Related: Classrooms Do you think English is a difficult language to learn?
Starting a question with 'Could you' vs. 'Can you'? [duplicate]
Instead, try to make it a more general question that would interest other English learners besides yourself. Also, when asking for a comparison between two sentences, highlight the difference. …
Which of 'Question on', 'question about', 'question regarding ...
"a question on" means: "a question on the topic of" and therefore can only be used when one can insert the phrase "the topic of" after the "on", while "a question about" can used before …
Conversation Questions for the ESL/EFL Classroom (I-TESL-J)
Interesting questions for discussions in Engish lessons. A Project of The Internet TESL Journal If this is your first time here, then read the Teacher's Guide to Using These Pages
When to use "is" vs. "does" when asking a question?
When the verb in a statement is neither a primary auxiliary verb (be, have, do) nor a modal auxiliary verb (will, would, can, could, may, might, shall, should, must, ought to, used to), do is …
ESL Conversation Questions - Restaurants & Eating Out (I-TESL-J)
Restaurants & Eating Out A Part of Conversation Questions for the ESL Classroom.. Related: Fruits and Vegetables, Vegetarian, Diets, Food & Eating, Tipping
ESL Conversation Questions - Food & Eating (I-TESL-J)
Food & Eating A Part of Conversation Questions for the ESL Classroom.. Related: Restaurants, Fruits and Vegetables, Vegetarian, Diets, Tipping
ESL Conversation Questions - The Art of Conversation (I-TESL-J)
A list of questions you can use to generate conversations in the ESL/EFL classroom.
ESL Conversation Questions - Hobbies (I-TESL-J)
A list of questions you can use to generate conversations in the ESL/EFL classroom.
ESL Conversation Questions - Do You Wish...? (I-TESL-J)
A list of questions you can use to generate conversations in the ESL/EFL classroom.
ESL Conversation Questions - English study (I-TESL-J)
English Study A Part of Conversation Questions for the ESL Classroom.. Related: Classrooms Do you think English is a difficult language to learn?
Starting a question with 'Could you' vs. 'Can you'? [duplicate]
Instead, try to make it a more general question that would interest other English learners besides yourself. Also, when asking for a comparison between two sentences, highlight the difference. …
